{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# XGBoost Classification Part 2"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yfinance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.485Z",
          "iopub.execute_input": "2021-09-18T02:46:25.496Z",
          "iopub.status.idle": "2021-09-18T02:46:25.508Z",
          "shell.execute_reply": "2021-09-18T02:46:24.953Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.525Z",
          "iopub.execute_input": "2021-09-18T02:46:25.535Z",
          "iopub.status.idle": "2021-09-18T02:46:25.570Z",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Create more data\n",
        "dataset['Increase/Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,-1)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,-1)\n",
        "dataset['Return'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Increase/Decrease  \\\nDate                                                                          \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200                  1   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n\n            Buy_Sell_on_Open  Buy_Sell    Return  \nDate                                              \n2014-01-03                 1         1  0.012658  \n2014-01-06                 1         1  0.032500  \n2014-01-07                 1        -1  0.012106  \n2014-01-08                -1        -1  0.000000  \n2014-01-09                -1         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase/Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Return</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.584Z",
          "iopub.execute_input": "2021-09-18T02:46:25.594Z",
          "shell.execute_reply": "2021-09-18T02:46:24.976Z",
          "iopub.status.idle": "2021-09-18T02:46:25.611Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "(1257, 10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.625Z",
          "iopub.execute_input": "2021-09-18T02:46:25.635Z",
          "iopub.status.idle": "2021-09-18T02:46:25.653Z",
          "shell.execute_reply": "2021-09-18T02:46:24.987Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low', 'Volume']].values\n",
        "y = dataset['Buy_Sell'].values"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.668Z",
          "iopub.execute_input": "2021-09-18T02:46:25.678Z",
          "iopub.status.idle": "2021-09-18T02:46:25.690Z",
          "shell.execute_reply": "2021-09-18T02:46:24.997Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with warnings.catch_warnings():\n",
        "    warnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n",
        "    from xgboost import XGBClassifier\n",
        "\n",
        "# XGboost algorithm\n",
        "model = XGBClassifier()"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.705Z",
          "iopub.execute_input": "2021-09-18T02:46:25.714Z",
          "iopub.status.idle": "2021-09-18T02:46:25.725Z",
          "shell.execute_reply": "2021-09-18T02:46:25.008Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.50, random_state=1)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.741Z",
          "iopub.execute_input": "2021-09-18T02:46:25.750Z",
          "iopub.status.idle": "2021-09-18T02:46:25.762Z",
          "shell.execute_reply": "2021-09-18T02:46:25.020Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_set  = [(X_train, y_train), (X_test,y_test)]"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.777Z",
          "iopub.execute_input": "2021-09-18T02:46:25.786Z",
          "iopub.status.idle": "2021-09-18T02:46:25.797Z",
          "shell.execute_reply": "2021-09-18T02:46:25.031Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, eval_metric='logloss', eval_set=evaluate_set)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-logloss:0.64969\tvalidation_1-logloss:0.69235\n",
            "[1]\tvalidation_0-logloss:0.61119\tvalidation_1-logloss:0.70193\n",
            "[2]\tvalidation_0-logloss:0.58345\tvalidation_1-logloss:0.71234\n",
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            "[92]\tvalidation_0-logloss:0.21893\tvalidation_1-logloss:0.94414\n",
            "[93]\tvalidation_0-logloss:0.21588\tvalidation_1-logloss:0.94834\n",
            "[94]\tvalidation_0-logloss:0.21521\tvalidation_1-logloss:0.94898\n",
            "[95]\tvalidation_0-logloss:0.21468\tvalidation_1-logloss:0.94999\n",
            "[96]\tvalidation_0-logloss:0.21287\tvalidation_1-logloss:0.95308\n",
            "[97]\tvalidation_0-logloss:0.21233\tvalidation_1-logloss:0.95457\n",
            "[98]\tvalidation_0-logloss:0.21114\tvalidation_1-logloss:0.95576\n",
            "[99]\tvalidation_0-logloss:0.20998\tvalidation_1-logloss:0.95743\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n       importance_type='gain', interaction_constraints='',\n       learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n       min_child_weight=1, missing=nan, monotone_constraints='()',\n       n_estimators=100, n_jobs=20, num_parallel_tree=1,\n       objective='binary:logistic', random_state=0, reg_alpha=0,\n       reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n       use_label_encoder=True, validate_parameters=1, verbosity=None)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:25.811Z",
          "iopub.execute_input": "2021-09-18T02:46:25.822Z",
          "iopub.status.idle": "2021-09-18T02:46:26.370Z",
          "shell.execute_reply": "2021-09-18T02:46:25.042Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "score = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy: %.3f' % score)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.499\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:26.385Z",
          "iopub.execute_input": "2021-09-18T02:46:26.395Z",
          "iopub.status.idle": "2021-09-18T02:46:26.411Z",
          "shell.execute_reply": "2021-09-18T02:46:25.054Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.evals_result()"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:26.426Z",
          "iopub.execute_input": "2021-09-18T02:46:26.437Z",
          "iopub.status.idle": "2021-09-18T02:46:26.449Z",
          "shell.execute_reply": "2021-09-18T02:46:25.065Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(results['validation_0']['logloss'], label='train')\n",
        "plt.plot(results['validation_1']['logloss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:26.464Z",
          "iopub.execute_input": "2021-09-18T02:46:26.473Z",
          "shell.execute_reply": "2021-09-18T02:46:25.077Z",
          "iopub.status.idle": "2021-09-18T02:46:26.492Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import metrics\n",
        "\n",
        "print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rmse of prediction is: 1.4153372920397715\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:26.507Z",
          "iopub.execute_input": "2021-09-18T02:46:26.517Z",
          "iopub.status.idle": "2021-09-18T02:46:26.535Z",
          "shell.execute_reply": "2021-09-18T02:46:25.087Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('XGBoost Classifier Score:', model.score(X_test, y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Classifier Score: 0.49920508744038156\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:26.552Z",
          "iopub.execute_input": "2021-09-18T02:46:26.562Z",
          "iopub.status.idle": "2021-09-18T02:46:26.578Z",
          "shell.execute_reply": "2021-09-18T02:46:25.100Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tune XGBoost Model Using Learning Curves"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBClassifier(n_estimators=500)"
      ],
      "outputs": [],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:26.595Z",
          "iopub.execute_input": "2021-09-18T02:46:26.604Z",
          "iopub.status.idle": "2021-09-18T02:46:26.616Z",
          "shell.execute_reply": "2021-09-18T02:46:25.110Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, eval_metric='logloss', eval_set=evaluate_set)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[439]\tvalidation_0-logloss:0.05559\tvalidation_1-logloss:1.33949\n",
            "[440]\tvalidation_0-logloss:0.05548\tvalidation_1-logloss:1.34000\n",
            "[441]\tvalidation_0-logloss:0.05533\tvalidation_1-logloss:1.34301\n",
            "[442]\tvalidation_0-logloss:0.05526\tvalidation_1-logloss:1.34328\n",
            "[443]\tvalidation_0-logloss:0.05506\tvalidation_1-logloss:1.34354\n",
            "[444]\tvalidation_0-logloss:0.05501\tvalidation_1-logloss:1.34310\n",
            "[445]\tvalidation_0-logloss:0.05495\tvalidation_1-logloss:1.34406\n",
            "[446]\tvalidation_0-logloss:0.05483\tvalidation_1-logloss:1.34386\n",
            "[447]\tvalidation_0-logloss:0.05476\tvalidation_1-logloss:1.34379\n",
            "[448]\tvalidation_0-logloss:0.05452\tvalidation_1-logloss:1.34511\n",
            "[449]\tvalidation_0-logloss:0.05447\tvalidation_1-logloss:1.34564\n",
            "[450]\tvalidation_0-logloss:0.05439\tvalidation_1-logloss:1.34629\n",
            "[451]\tvalidation_0-logloss:0.05433\tvalidation_1-logloss:1.34531\n",
            "[452]\tvalidation_0-logloss:0.05408\tvalidation_1-logloss:1.34631\n",
            "[453]\tvalidation_0-logloss:0.05400\tvalidation_1-logloss:1.34628\n",
            "[454]\tvalidation_0-logloss:0.05372\tvalidation_1-logloss:1.34850\n",
            "[455]\tvalidation_0-logloss:0.05363\tvalidation_1-logloss:1.34818\n",
            "[456]\tvalidation_0-logloss:0.05357\tvalidation_1-logloss:1.34890\n",
            "[457]\tvalidation_0-logloss:0.05345\tvalidation_1-logloss:1.34921\n",
            "[458]\tvalidation_0-logloss:0.05338\tvalidation_1-logloss:1.34960\n",
            "[459]\tvalidation_0-logloss:0.05321\tvalidation_1-logloss:1.35087\n",
            "[460]\tvalidation_0-logloss:0.05313\tvalidation_1-logloss:1.35112\n",
            "[461]\tvalidation_0-logloss:0.05301\tvalidation_1-logloss:1.35126\n",
            "[462]\tvalidation_0-logloss:0.05286\tvalidation_1-logloss:1.35122\n",
            "[463]\tvalidation_0-logloss:0.05277\tvalidation_1-logloss:1.35150\n",
            "[464]\tvalidation_0-logloss:0.05264\tvalidation_1-logloss:1.35202\n",
            "[465]\tvalidation_0-logloss:0.05257\tvalidation_1-logloss:1.35286\n",
            "[466]\tvalidation_0-logloss:0.05250\tvalidation_1-logloss:1.35372\n",
            "[467]\tvalidation_0-logloss:0.05233\tvalidation_1-logloss:1.35441\n",
            "[468]\tvalidation_0-logloss:0.05225\tvalidation_1-logloss:1.35494\n",
            "[469]\tvalidation_0-logloss:0.05204\tvalidation_1-logloss:1.35575\n",
            "[470]\tvalidation_0-logloss:0.05187\tvalidation_1-logloss:1.35644\n",
            "[471]\tvalidation_0-logloss:0.05181\tvalidation_1-logloss:1.35703\n",
            "[472]\tvalidation_0-logloss:0.05163\tvalidation_1-logloss:1.35807\n",
            "[473]\tvalidation_0-logloss:0.05151\tvalidation_1-logloss:1.35928\n",
            "[474]\tvalidation_0-logloss:0.05146\tvalidation_1-logloss:1.35973\n",
            "[475]\tvalidation_0-logloss:0.05136\tvalidation_1-logloss:1.36094\n",
            "[476]\tvalidation_0-logloss:0.05124\tvalidation_1-logloss:1.36235\n",
            "[477]\tvalidation_0-logloss:0.05119\tvalidation_1-logloss:1.36309\n",
            "[478]\tvalidation_0-logloss:0.05113\tvalidation_1-logloss:1.36233\n",
            "[479]\tvalidation_0-logloss:0.05107\tvalidation_1-logloss:1.36254\n",
            "[480]\tvalidation_0-logloss:0.05102\tvalidation_1-logloss:1.36259\n",
            "[481]\tvalidation_0-logloss:0.05095\tvalidation_1-logloss:1.36360\n",
            "[482]\tvalidation_0-logloss:0.05090\tvalidation_1-logloss:1.36354\n",
            "[483]\tvalidation_0-logloss:0.05084\tvalidation_1-logloss:1.36387\n",
            "[484]\tvalidation_0-logloss:0.05070\tvalidation_1-logloss:1.36519\n",
            "[485]\tvalidation_0-logloss:0.05065\tvalidation_1-logloss:1.36503\n",
            "[486]\tvalidation_0-logloss:0.05051\tvalidation_1-logloss:1.36593\n",
            "[487]\tvalidation_0-logloss:0.05045\tvalidation_1-logloss:1.36693\n",
            "[488]\tvalidation_0-logloss:0.05034\tvalidation_1-logloss:1.36767\n",
            "[489]\tvalidation_0-logloss:0.05029\tvalidation_1-logloss:1.36816\n",
            "[490]\tvalidation_0-logloss:0.05015\tvalidation_1-logloss:1.36976\n",
            "[491]\tvalidation_0-logloss:0.05010\tvalidation_1-logloss:1.37045\n",
            "[492]\tvalidation_0-logloss:0.05005\tvalidation_1-logloss:1.36974\n",
            "[493]\tvalidation_0-logloss:0.04986\tvalidation_1-logloss:1.37278\n",
            "[494]\tvalidation_0-logloss:0.04980\tvalidation_1-logloss:1.37268\n",
            "[495]\tvalidation_0-logloss:0.04965\tvalidation_1-logloss:1.37355\n",
            "[496]\tvalidation_0-logloss:0.04959\tvalidation_1-logloss:1.37299\n",
            "[497]\tvalidation_0-logloss:0.04951\tvalidation_1-logloss:1.37414\n",
            "[498]\tvalidation_0-logloss:0.04938\tvalidation_1-logloss:1.37437\n",
            "[499]\tvalidation_0-logloss:0.04931\tvalidation_1-logloss:1.37374\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n       importance_type='gain', interaction_constraints='',\n       learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n       min_child_weight=1, missing=nan, monotone_constraints='()',\n       n_estimators=500, n_jobs=20, num_parallel_tree=1,\n       objective='binary:logistic', random_state=0, reg_alpha=0,\n       reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n       use_label_encoder=True, validate_parameters=1, verbosity=None)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:26.631Z",
          "iopub.execute_input": "2021-09-18T02:46:26.641Z",
          "iopub.status.idle": "2021-09-18T02:46:29.544Z",
          "shell.execute_reply": "2021-09-18T02:46:25.122Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "score = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy: %.3f' % score)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.490\n"
          ]
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:29.561Z",
          "iopub.execute_input": "2021-09-18T02:46:29.570Z",
          "iopub.status.idle": "2021-09-18T02:46:29.586Z",
          "shell.execute_reply": "2021-09-18T02:46:25.133Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.evals_result()"
      ],
      "outputs": [],
      "execution_count": 18,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:29.600Z",
          "iopub.execute_input": "2021-09-18T02:46:29.611Z",
          "iopub.status.idle": "2021-09-18T02:46:29.622Z",
          "shell.execute_reply": "2021-09-18T02:46:25.144Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(results['validation_0']['logloss'], label='train')\n",
        "plt.plot(results['validation_1']['logloss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:29.636Z",
          "iopub.execute_input": "2021-09-18T02:46:29.647Z",
          "iopub.status.idle": "2021-09-18T02:46:29.663Z",
          "shell.execute_reply": "2021-09-18T02:46:25.154Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import metrics\n",
        "\n",
        "print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rmse of prediction is: 1.4287531113177527\n"
          ]
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:29.678Z",
          "iopub.execute_input": "2021-09-18T02:46:29.687Z",
          "iopub.status.idle": "2021-09-18T02:46:29.703Z",
          "shell.execute_reply": "2021-09-18T02:46:25.165Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('XGBoost Classifier Score:', model.score(X_test, y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Classifier Score: 0.48966613672496023\n"
          ]
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:29.716Z",
          "iopub.execute_input": "2021-09-18T02:46:29.727Z",
          "iopub.status.idle": "2021-09-18T02:46:29.745Z",
          "shell.execute_reply": "2021-09-18T02:46:25.177Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tune XGBoost Model Using Learning Curves Part 2"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBClassifier(n_estimators=500, eta=0.05)"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:29.759Z",
          "iopub.execute_input": "2021-09-18T02:46:29.770Z",
          "iopub.status.idle": "2021-09-18T02:46:29.781Z",
          "shell.execute_reply": "2021-09-18T02:46:25.187Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, eval_metric='logloss', eval_set=evaluate_set)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[460]\tvalidation_0-logloss:0.25478\tvalidation_1-logloss:0.88121\n",
            "[461]\tvalidation_0-logloss:0.25470\tvalidation_1-logloss:0.88128\n",
            "[462]\tvalidation_0-logloss:0.25457\tvalidation_1-logloss:0.88131\n",
            "[463]\tvalidation_0-logloss:0.25424\tvalidation_1-logloss:0.88175\n",
            "[464]\tvalidation_0-logloss:0.25409\tvalidation_1-logloss:0.88208\n",
            "[465]\tvalidation_0-logloss:0.25379\tvalidation_1-logloss:0.88242\n",
            "[466]\tvalidation_0-logloss:0.25298\tvalidation_1-logloss:0.88326\n",
            "[467]\tvalidation_0-logloss:0.25252\tvalidation_1-logloss:0.88369\n",
            "[468]\tvalidation_0-logloss:0.25214\tvalidation_1-logloss:0.88415\n",
            "[469]\tvalidation_0-logloss:0.25151\tvalidation_1-logloss:0.88426\n",
            "[470]\tvalidation_0-logloss:0.25123\tvalidation_1-logloss:0.88455\n",
            "[471]\tvalidation_0-logloss:0.25082\tvalidation_1-logloss:0.88518\n",
            "[472]\tvalidation_0-logloss:0.25033\tvalidation_1-logloss:0.88527\n",
            "[473]\tvalidation_0-logloss:0.24979\tvalidation_1-logloss:0.88589\n",
            "[474]\tvalidation_0-logloss:0.24952\tvalidation_1-logloss:0.88654\n",
            "[475]\tvalidation_0-logloss:0.24939\tvalidation_1-logloss:0.88657\n",
            "[476]\tvalidation_0-logloss:0.24900\tvalidation_1-logloss:0.88686\n",
            "[477]\tvalidation_0-logloss:0.24853\tvalidation_1-logloss:0.88726\n",
            "[478]\tvalidation_0-logloss:0.24818\tvalidation_1-logloss:0.88777\n",
            "[479]\tvalidation_0-logloss:0.24780\tvalidation_1-logloss:0.88841\n",
            "[480]\tvalidation_0-logloss:0.24736\tvalidation_1-logloss:0.88893\n",
            "[481]\tvalidation_0-logloss:0.24691\tvalidation_1-logloss:0.88945\n",
            "[482]\tvalidation_0-logloss:0.24640\tvalidation_1-logloss:0.89024\n",
            "[483]\tvalidation_0-logloss:0.24593\tvalidation_1-logloss:0.89067\n",
            "[484]\tvalidation_0-logloss:0.24521\tvalidation_1-logloss:0.89099\n",
            "[485]\tvalidation_0-logloss:0.24480\tvalidation_1-logloss:0.89112\n",
            "[486]\tvalidation_0-logloss:0.24464\tvalidation_1-logloss:0.89119\n",
            "[487]\tvalidation_0-logloss:0.24402\tvalidation_1-logloss:0.89175\n",
            "[488]\tvalidation_0-logloss:0.24387\tvalidation_1-logloss:0.89179\n",
            "[489]\tvalidation_0-logloss:0.24370\tvalidation_1-logloss:0.89191\n",
            "[490]\tvalidation_0-logloss:0.24351\tvalidation_1-logloss:0.89203\n",
            "[491]\tvalidation_0-logloss:0.24344\tvalidation_1-logloss:0.89210\n",
            "[492]\tvalidation_0-logloss:0.24317\tvalidation_1-logloss:0.89246\n",
            "[493]\tvalidation_0-logloss:0.24289\tvalidation_1-logloss:0.89318\n",
            "[494]\tvalidation_0-logloss:0.24277\tvalidation_1-logloss:0.89321\n",
            "[495]\tvalidation_0-logloss:0.24260\tvalidation_1-logloss:0.89333\n",
            "[496]\tvalidation_0-logloss:0.24252\tvalidation_1-logloss:0.89342\n",
            "[497]\tvalidation_0-logloss:0.24232\tvalidation_1-logloss:0.89398\n",
            "[498]\tvalidation_0-logloss:0.24189\tvalidation_1-logloss:0.89464\n",
            "[499]\tvalidation_0-logloss:0.24140\tvalidation_1-logloss:0.89546\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bynode=1, colsample_bytree=1, eta=0.05, gamma=0,\n       gpu_id=-1, importance_type='gain', interaction_constraints='',\n       learning_rate=0.0500000007, max_delta_step=0, max_depth=6,\n       min_child_weight=1, missing=nan, monotone_constraints='()',\n       n_estimators=500, n_jobs=20, num_parallel_tree=1,\n       objective='binary:logistic', random_state=0, reg_alpha=0,\n       reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n       use_label_encoder=True, validate_parameters=1, verbosity=None)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:29.795Z",
          "iopub.execute_input": "2021-09-18T02:46:29.805Z",
          "iopub.status.idle": "2021-09-18T02:46:32.795Z",
          "shell.execute_reply": "2021-09-18T02:46:25.197Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "score = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy: %.3f' % score)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.515\n"
          ]
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:32.812Z",
          "iopub.execute_input": "2021-09-18T02:46:32.826Z",
          "iopub.status.idle": "2021-09-18T02:46:32.846Z",
          "shell.execute_reply": "2021-09-18T02:46:25.207Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.evals_result()"
      ],
      "outputs": [],
      "execution_count": 25,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:32.865Z",
          "iopub.execute_input": "2021-09-18T02:46:32.876Z",
          "iopub.status.idle": "2021-09-18T02:46:32.889Z",
          "shell.execute_reply": "2021-09-18T02:46:25.217Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(results['validation_0']['logloss'], label='train')\n",
        "plt.plot(results['validation_1']['logloss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 26,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:32.905Z",
          "iopub.execute_input": "2021-09-18T02:46:32.915Z",
          "iopub.status.idle": "2021-09-18T02:46:32.933Z",
          "shell.execute_reply": "2021-09-18T02:46:25.228Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import metrics\n",
        "\n",
        "print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rmse of prediction is: 1.3926904341844955\n"
          ]
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:32.950Z",
          "iopub.execute_input": "2021-09-18T02:46:32.959Z",
          "iopub.status.idle": "2021-09-18T02:46:32.976Z",
          "shell.execute_reply": "2021-09-18T02:46:25.238Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('XGBoost Classifier Score:', model.score(X_test, y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Classifier Score: 0.5151033386327504\n"
          ]
        }
      ],
      "execution_count": 28,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-18T02:46:32.992Z",
          "iopub.execute_input": "2021-09-18T02:46:33.003Z",
          "iopub.status.idle": "2021-09-18T02:46:33.021Z",
          "shell.execute_reply": "2021-09-18T02:46:25.251Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tune XGBoost Model Using Learning Curves Part 3"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBClassifier(n_estimators=2000, eta=0.05)"
      ],
      "outputs": [],
      "execution_count": 29,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.264Z",
          "iopub.status.busy": "2021-09-18T02:46:33.036Z",
          "iopub.execute_input": "2021-09-18T02:46:33.047Z",
          "iopub.status.idle": "2021-09-18T02:46:33.059Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, eval_metric='logloss', eval_set=evaluate_set)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bynode=1, colsample_bytree=1, eta=0.05, gamma=0,\n       gpu_id=-1, importance_type='gain', interaction_constraints='',\n       learning_rate=0.0500000007, max_delta_step=0, max_depth=6,\n       min_child_weight=1, missing=nan, monotone_constraints='()',\n       n_estimators=2000, n_jobs=20, num_parallel_tree=1,\n       objective='binary:logistic', random_state=0, reg_alpha=0,\n       reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n       use_label_encoder=True, validate_parameters=1, verbosity=None)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 30,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.276Z",
          "iopub.status.busy": "2021-09-18T02:46:33.074Z",
          "iopub.execute_input": "2021-09-18T02:46:33.083Z",
          "iopub.status.idle": "2021-09-18T02:46:48.556Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "score = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy: %.3f' % score)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.501\n"
          ]
        }
      ],
      "execution_count": 31,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.287Z",
          "iopub.status.busy": "2021-09-18T02:46:48.575Z",
          "iopub.execute_input": "2021-09-18T02:46:48.586Z",
          "iopub.status.idle": "2021-09-18T02:46:48.607Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.evals_result()"
      ],
      "outputs": [],
      "execution_count": 32,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.299Z",
          "iopub.status.busy": "2021-09-18T02:46:48.626Z",
          "iopub.execute_input": "2021-09-18T02:46:48.637Z",
          "iopub.status.idle": "2021-09-18T02:46:48.650Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(results['validation_0']['logloss'], label='train')\n",
        "plt.plot(results['validation_1']['logloss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 33,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.309Z",
          "iopub.status.busy": "2021-09-18T02:46:48.668Z",
          "iopub.execute_input": "2021-09-18T02:46:48.681Z",
          "iopub.status.idle": "2021-09-18T02:46:48.701Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import metrics\n",
        "\n",
        "print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rmse of prediction is: 1.4130889390839936\n"
          ]
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.319Z",
          "iopub.status.busy": "2021-09-18T02:46:48.719Z",
          "iopub.execute_input": "2021-09-18T02:46:48.729Z",
          "iopub.status.idle": "2021-09-18T02:46:48.749Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('XGBoost Classifier Score:', model.score(X_test, y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Classifier Score: 0.5007949125596184\n"
          ]
        }
      ],
      "execution_count": 35,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.329Z",
          "iopub.status.busy": "2021-09-18T02:46:48.766Z",
          "iopub.execute_input": "2021-09-18T02:46:48.778Z",
          "iopub.status.idle": "2021-09-18T02:46:48.799Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tune XGBoost Model Using Learning Curves Part 4"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBClassifier(n_estimators=2000, eta=0.05, subsample=0.5, colsample_bytree=0.5)"
      ],
      "outputs": [],
      "execution_count": 36,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.342Z",
          "iopub.status.busy": "2021-09-18T02:46:48.816Z",
          "iopub.execute_input": "2021-09-18T02:46:48.827Z",
          "iopub.status.idle": "2021-09-18T02:46:48.842Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, eval_metric='logloss', eval_set=evaluate_set)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n       colsample_bynode=1, colsample_bytree=0.5, eta=0.05, gamma=0,\n       gpu_id=-1, importance_type='gain', interaction_constraints='',\n       learning_rate=0.0500000007, max_delta_step=0, max_depth=6,\n       min_child_weight=1, missing=nan, monotone_constraints='()',\n       n_estimators=2000, n_jobs=20, num_parallel_tree=1,\n       objective='binary:logistic', random_state=0, reg_alpha=0,\n       reg_lambda=1, scale_pos_weight=1, subsample=0.5,\n       tree_method='exact', use_label_encoder=True, validate_parameters=1,\n       verbosity=None)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 37,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.353Z",
          "iopub.status.busy": "2021-09-18T02:46:48.861Z",
          "iopub.execute_input": "2021-09-18T02:46:48.873Z",
          "iopub.status.idle": "2021-09-18T02:47:05.779Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "score = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy: %.3f' % score)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.504\n"
          ]
        }
      ],
      "execution_count": 38,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.363Z",
          "iopub.status.busy": "2021-09-18T02:47:05.801Z",
          "iopub.execute_input": "2021-09-18T02:47:05.815Z",
          "iopub.status.idle": "2021-09-18T02:47:05.839Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = model.evals_result()"
      ],
      "outputs": [],
      "execution_count": 39,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.375Z",
          "iopub.status.busy": "2021-09-18T02:47:05.858Z",
          "iopub.execute_input": "2021-09-18T02:47:05.870Z",
          "iopub.status.idle": "2021-09-18T02:47:05.886Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(results['validation_0']['logloss'], label='train')\n",
        "plt.plot(results['validation_1']['logloss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 40,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.385Z",
          "iopub.status.busy": "2021-09-18T02:47:05.909Z",
          "iopub.execute_input": "2021-09-18T02:47:05.921Z",
          "iopub.status.idle": "2021-09-18T02:47:05.943Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import metrics\n",
        "\n",
        "print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The rmse of prediction is: 1.4085814668692866\n"
          ]
        }
      ],
      "execution_count": 41,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-18T02:46:25.396Z",
          "iopub.status.busy": "2021-09-18T02:47:05.962Z",
          "iopub.execute_input": "2021-09-18T02:47:05.976Z",
          "iopub.status.idle": "2021-09-18T02:47:05.996Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('XGBoost Classifier Score:', model.score(X_test, y_test))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Classifier Score: 0.5039745627980922\n"
          ]
        }
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